"""
财报季事件驱动策略
策略逻辑：
1. 获取上市公司财报日历
2. 在财报公布前建仓
3. 根据历史财报后表现决定方向
4. 财报公布后平仓
"""

import pandas as pd
from datetime import timedelta

class EarningsSeasonStrategy:
    def __init__(self, ticker, lookback_years=3):
        self.ticker = ticker
        self.lookback = lookback_years
        self.earnings_calendar = self.load_earnings_calendar()
        self.historical_performance = self.analyze_historical_performance()
        
    def load_earnings_calendar(self):
        """加载财报日历数据"""
        # 这里可以从API或数据库获取实际数据
        return pd.DataFrame({
            'date': pd.to_datetime(['2023-01-15', '2023-04-15', '2023-07-15', '2023-10-15']),
            'eps_estimate': [1.2, 1.3, 1.4, 1.5],
            'revenue_estimate': [1000, 1100, 1200, 1300]
        })
    
    def analyze_historical_performance(self):
        """分析历史财报后表现"""
        # 模拟数据 - 实际应用中应从历史数据计算
        return {
            'avg_1d_return': 0.015,  # 财报后1日平均回报
            'beat_probability': 0.65,  # 超预期概率
            'post_earnings_drift': 0.02  # 财报后漂移(20天)
        }
    
    def generate_signals(self, current_date, current_price):
        """生成交易信号"""
        signals = []
        
        # 检查未来14天内是否有财报
        upcoming_earnings = self.earnings_calendar[
            (self.earnings_calendar['date'] > current_date) & 
            (self.earnings_calendar['date'] <= current_date + timedelta(days=14))
        ]
        
        for _, row in upcoming_earnings.iterrows():
            # 财报前5天建仓
            if (row['date'] - current_date).days == 5:
                direction = 1 if self.historical_performance['avg_1d_return'] > 0 else -1
                signals.append({
                    'date': current_date,
                    'ticker': self.ticker,
                    'signal': direction,
                    'strength': abs(self.historical_performance['avg_1d_return']),
                    'expiration': row['date']
                })
            
            # 财报当天平仓
            if row['date'] == current_date:
                signals.append({
                    'date': current_date,
                    'ticker': self.ticker,
                    'signal': 0,
                    'reason': 'Earnings release'
                })
        
        return signals

if __name__ == '__main__':
    strategy = EarningsSeasonStrategy('AAPL')
    
    # 模拟运行
    test_dates = pd.date_range('2023-01-01', '2023-01-20')
    for date in test_dates:
        signals = strategy.generate_signals(date, 150)
        if signals:
            print(f"{date.date()}: {signals}")